Optimization may be pertinent to many business scenarios or other applications. For instance, business owners often desire to optimize gross revenue, net revenue, profit, sales volume, etc. These goals change from time to time, as circumstances suggest, and may apply to the entire business, a subdivision of the overall organization such as a subsidiary, a division, a department, a product line, individual products, etc. These same goals may be directed toward particular customer segments based on demographic data, or other geographic, income, age, or other distinctions in the customer population.
In certain circumstances a business may, for a variety of reasons, seek to substantially optimize one metric of their performance while also satisfying a goal as measured by another metric. An example of these multi-metric optimization goals is maximizing one metric while satisfying a minimum level of performance as measured by a second metric. While the ability to optimize performance as measured by one metric can be difficult, optimizing performance as measured by two metrics is more difficult still. Indeed, satisfying one goal might worsen performance related to the first goal.
Personnel associated with a business may wish to explore a number of alternatives to achieving their goals. Often, they use computerized models to evaluate the effects of various pricing strategies (such as changing a pricing list). These models may utilize historic market data, data concerning market response to various stimuli, potentially numerous metrics, linear and non-linear constraints, various analytic tools, etc. The output of these models often includes graphs which portray relationships between master metrics and trade off metrics in the form of various efficient frontiers (where master metrics can be those metrics which users may wish to optimize while satisfying some constraint on another metric, which can be termed the trade off metric). Business personnel use efficient frontiers to evaluate various options available to them to optimize aspects of the business. Propagating even a relatively small change through such complex models can consume significant processing resources associated with the models. Indeed, modifications to these models may have to occur when processing resources are free (such as at night), in part, to avoid monopolizing these resources. Thus, business personnel may be limited in their ability to use efficient frontiers in their efforts to optimize their businesses due to limitations in computational infrastructure.